In the actual algorithm, we used the DBSCAN [1]}, [2]} density-based clustering that runs swiftly on our problem and returns accurate solutions.
DBSCAN requires two parameters, , the minimum size \(c_\text{min}\)  of a cluster to be kept and a threshold \(\epsilon _\text{T}\)  to decide if two model instances are neighbors in the consensus space.
The minimum size \(c_\text{min} = 1\)  since single-element clusters also contain dominant model instances and, thus, should be kept.
The setting of threshold \(\epsilon _\text{T}\)  is intuitive and easy.
In most of our experiments, we used \(\epsilon _\text{T} = 0.2\) , considering two instances neighbors if their Tanimoto distance is smaller than \(0.2\) .
This can roughly be interpreted as considering two model instances similar if more than \(20\%\)  of their inliers are shared.
